code: def forward(self, x):
      x = self.patch_embed(x)
      Wh, Ww = x.size(2), x.size(3)
  
      x = x.flatten(2).transpose(1, 2)
      x = self.pos_drop(x)
  
      outs = {}
      for i in range(self.num_layers):
          layer = self.layers[i]
          x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
          if i in self.out_indices:
              norm_layer = getattr(self, f"norm{i}")
              x_out = norm_layer(x_out)
  
              out = (
                  x_out.view(-1, H, W, self.num_features[i])
                  .permute(0, 3, 1, 2)
                  .contiguous()
              )
              outs["res{}".format(i + 2)] = out
      return outs
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn.bricks import DropPath
  from mmdet.registry import MODELS
label_desc: Use image node_id 2 for calling the FocalNet obj's underlying member method code with example usage: obj.forward(x) and returns: outs